A “supply chain” refers to a chain of entities, paths, and other points across which a raw material, part, or product is processed, transferred, and/or otherwise manipulated. Typically, the supply chain for a business enterprise includes suppliers, manufacturing centers, warehouses, distribution centers, and retail outlets. Efficient and cost-effective supply chain management requires controlling the flow and storage of raw materials, work-in-process inventory and finished products within and between various facilities. The goal of a properly managed supply chain is to allow merchandise to be produced and distributed in the right quantities, to the right locations and at the right time, while minimizing system-wide costs in order to satisfying customer service expectations.
Advances in data warehousing provide the ability to store detailed event information across all aspects of the supply chain. Properly correlating and analyzing this data can result in improved customer service without increased costs and inventory—effectively trading information for inventory.
Teradata, a division of NCR Corporation, has developed a suite of analytical applications, referred to as Teradata Supply Chain Intelligence (SCI), to assist manufacturing, retail and transportation companies striving to create efficient flows of new and existing, cost-effective products. The solution helps address several critical challenges facing the retail industry, including product out-of-stocks, inventory turns, security and transportation efficiency.
As illustrated in FIG. 1, the Teradata Supply Chain Intelligence analytical application suite 101 is shown to be part of a data warehouse solution built upon NCR Corporation's Teradata Data Warehouse 103, using a Teradata Supply Chain Logical Data Model (LDM) 105. The Teradata Data Warehouse 103 provides a framework for tracking and reporting on extremely complex product flows associated with manufacturing and supply chain.
The Teradata Supply Chain Intelligence application suite 103 includes four key components: Procurement application 109, Production application 111, Logistics application 107 and Fulfillment application 113. Procurement application 109 is utilized to optimize purchasing efforts by evaluating global sourcing strategies and managing the most effective supplier base. Production application 111 provides managers and executives with the ability to measure and improve the way they convert the factors of production, e.g., raw materials, labor and capacity, into finished goods. Fulfillment application 113 is used to optimize customer service levels and margins through predictive inventory analysis and profitability management. Advanced Logistics application 107 provides the ability to link and manage transportation, inventories, and logistic operations across your extended supply chain.
Although integration of a data warehouse into a supply chain provides many benefits, the availability of data is typically limited to entities that are under the control of the organization or willing to cooperate. Providing the best product and customer service requires an understanding of information that may not be directly obtainable.
FIG. 2 illustrates a simplified supply chain integrated with a data warehouse. Product movements are represented by arrows 230 and information transmissions are shown by arrows 240. Product moves through the supply chain from suppliers/factory 203 to warehouse 205 and retail centers (or OEM distributors) 209 to consumers 211. Also shown are return/repair centers 217 which receive unsold inventory 213 from retail centers 209 and return product 215 from consumers 211. In this example, factory 205, warehouse 207 and retail centers 209 and return centers 217 are all linked via a data warehouse 201. The area contained in box 220 represents information that is generally not made available to the warehouse. Note that this may include parts of retail centers 209 and return centers 217.
Frequently, supply chain analysis ends at the last available data collection point—generally with a retail or OEM delivery. Once a product is at the retail level, the amount of information available to a data warehouse varies with the sophistication of the retailer. While point-of-sale information is becoming more accessible, it is not always available to the supply chain integrator, often because of privacy or competitive advantage issues.
When the product reaches the consumer, there is generally little data available to the data warehouse. Registration and customer service requests provide, at best, limited information.
The supply chain integration needs to provide more accurate evaluations of use profiles and product distribution at the end customer. Customer returns and rejects are beset with uncertainties. Some of the questions difficult to answer once a product leaves the control of the manufacturer or retailer include:                ‘Was the product defective prior to purchase’—suggesting quality control, handling and/or storage issues?        ‘Was the product properly installed and failed after use—or was there an installation issue?’        ‘Is there evidence of customer fraud—was a used product returned as new?’        ‘Was the product used for a reasonable amount of time?’        ‘What is the typical time between purchase and use of the product?’        ‘What is a typical use profile?’        
Providing data to support answering these questions will help the manufacturer/supply chain manager evaluate current policies, product documentation, returns, etc. and improve overall customer satisfaction.
Additionally, many products may have limited shelf life or other specific inventory requirements. Also, many products may be returned as defective when in reality handling and/or storage issues are to blame. The data to address these issues is typically not available as a direct feed to the data warehouse.
Supply Chain optimization is always based on a balance of demand patterns against inventory levels and production/shipments, with a goal to maximize customer service while minimizing inventory and costs. Unfortunately, demand signals tend to be provided only at the retail/OEM level, and not actual customer demand. Direct observation of customer demand is difficult to obtain, but evaluation of inventory shelf life can provide an indicator of demand changes.